Vepakomma, Praneeth

14 publications

ICLRW 2025 FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma

ICLRW 2025 Initialization Using Update Approximation Is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov, Samuel Horváth, Praneeth Vepakomma

TMLR 2025 Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach Chaouki Ben Issaid, Praneeth Vepakomma, Mehdi Bennis

NeurIPS 2024 Data Acquisition via Experimental Design for Data Markets Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar

ECCV 2024 DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar

NeurIPSW 2024 FedEx-LoRA: Exact Aggregation for Federated Parameter-Efficient Fine-Tuning of Foundation Models Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma

TMLR 2024 Privacy-Preserving Split Learning with Vision Transformers Using Patch-Wise Random and Noisy CutMix Seungeun Oh, Sihun Baek, Jihong Park, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim

TMLR 2023 Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with Log-Euclidean Metric Saiteja Utpala, Praneeth Vepakomma, Nina Miolane

NeurIPS 2023 Posthoc Privacy Guarantees for Collaborative Inference with Modified Propose-Test-Release Abhishek Singh, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar

ECCV 2022 Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar

NeurIPSW 2022 Differentially Private CutMix for Split Learning with Vision Transformer Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim

AAAI 2022 PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval Praneeth Vepakomma, Julia Balla, Ramesh Raskar

FnTML 2021 Advances and Open Problems in Federated Learning Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

CVPR 2021 DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks Abhishek Singh, Ayush Chopra, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar